Factorial Regression in JASP: Easy Post Hoc Analysis

Welcome to understanding factorial regression in JASP!Let's compare simple regression with factorial regression to understand the key differences.Simple regression examines the relationship between one independent variable and one dependent variable.Factorial regression, however, can handle multiple independent variables and their interactions simultaneously.In factorial regression, we work with different types of variables.One of the key features is the ability to examine interaction effects between variables.Let's look at the key advantages of using factorial regression in your analysis.Factorial regression excels at handling categorical predictors, where different groups show different patterns.To set up factorial regression in JASP, we'll start by navigating to the correct analysis menu.First, load your dataset into JASP. Make sure your variables are properly coded, especially categorical variables which should be defined as nominal or ordinal.In the analysis window, you'll see two main fields. The Dependent Variable field is where you'll place your outcome variable.The Covariates field is where you'll add your predictor variables. For factorial regression, these can include both continuous and categorical variables.Next, navigate to the Model tab. Here you can specify the components of your factorial analysis.The Model Specification section allows you to select which effects to include. Start with main effects, which examine each variable's individual influence.You can also include interaction effects, which show how variables work together to influence your outcome.For more complex designs, you might want to include higher-order interactions, though these can be harder to interpret.The intercept term is typically included by default, representing the model's baseline value.With your model properly specified, you're ready to move on to configuring post-hoc tests and examining specific comparisons between groups.In JASP's post hoc test configuration, you'll find several important options to customize your analysis.First, select the variables you want to analyze further. These are typically your categorical independent variables.JASP offers various correction methods for multiple comparisons. Each method has its own strengths and is suited for different situations.Let's compare these correction methods to understand when to use each one.You can also enable various descriptive statistics to get detailed information about each factor level.When choosing a correction method, consider your sample size and research design.With these settings configured, you're ready to interpret your results.Post hoc analysis helps us understand specific differences between groups after finding significant effects in our factorial regression.In this table, we can see pairwise comparisons between our groups. Asterisks indicate statistically significant differences.Let's examine the first comparison between Control and Treatment 1. The mean difference of 2.45 is significant at the p less than 0.05 level.Confidence intervals help us understand the precision of these differences. A narrower interval indicates more precise estimation.Notice how the confidence interval for Treatment 1 versus Treatment 2 crosses zero, indicating a non-significant difference.Beyond statistical significance, we should consider effect sizes to understand practical significance.When interpreting post hoc results, remember these key points for a comprehensive analysis.By considering all these aspects, you can make well-informed conclusions about group differences in your factorial regression analysis.